A digital camera is a component often included in commercial electronic media device platforms. Digital cameras are now available in wearable form factors (e.g., video capture earpieces, video capture headsets, video capture eyeglasses, etc.), as well as embedded within smartphones, tablet computers, and notebook computers, etc.
The introduction of streaming video from mobile digital cameras has ushered in an era with unprecedented volumes of video data shared between mobile devices. Consider an application where the user wears a pair of glasses fitted with a video camera. The camera captures video streams depicting the activities of the user throughout the day. Much of that data will capture human subjects. Since the introduction of digital image processing decades ago many users have become accustomed to reducing wrinkles, freckles, and various blemishes from human subjects for a more visually appealing image or video. There are several commercial image processing software packages with which users can remove wrinkles, freckles, etc. and adjust skin tone. However, these image processing software packages typically require so much user interaction and time that their use is intractable for the large amounts of image data now being generated.
Automated skin-smoothing image enhancement techniques have not kept pace with the need, particularly in the low-cost, and low-power market sector that includes wearable computing platforms and mobile communication handsets. There has been considerable research on fast and automated methods for skin smoothing. One currently popular technique is an edge-preserving filtering called a ‘bilateral filter.’ However, a bilateral filter has a high computational cost/complexity necessitating a powerful CPU and GPU to process high resolution images (e.g., full HD) in real-time (e.g., at 30+ frames per second). Since sharing images between mobile devices has become popular, a powerful CPU and GPU is not always available. Hence, many of the platforms responsible for generating the vast majority of a user's archival image data are thus far ill-equipped to perform sophisticated image processing.
Automated image data enhancement that can implemented by ultra light, low-cost, and low-power platforms in real time with a video stream captured at potentially high frame rates (e.g., 30 frames/second, or more) is therefore highly advantageous.